Abstract
<jats:p>Weed detection plays an important role in effective weed management strategies, contributing to enhanced crop yield and reduced reliance on herbicides. Traditional methods of weed detection mostly suffer from limitations in accuracy, efficiency, and scalability. Manual inspection and monitoring are labour-intensive and time-consuming, deterring their practicality for large-scale farming operations. In recent years, the emergence of computer vision techniques has changed the field of weed detection. Leveraging advances in machine learning and image processing, computer vision offers automated and efficient solutions for weed detection. Among various available techniques, detection using bounding boxes proved significant due to its effectiveness in localizing and distinguishing weeds within crop images. This article explores the role of computer vision in weed detection, specifically focusing on bounding boxes. The article highlights the significance of computer vision techniques, particularly detection using bounding boxes, in automating weed detection processes.</jats:p>